OpenRaiser/Intern-Atlas huggingface dataset:
Luminance Component Analysis for Exposure Correction
Venue: arXiv cs.CV 2024
Year: 2024

Exposure correction methods aim to adjust the luminance while maintaining other luminance-unrelated information. However, current exposure correction methods have difficulty in fully separating luminance-related and luminance-unrelated components, leading to distortions in color, loss of detail, and requiring extra restoration procedures. Inspired by principal component analysis (PCA), this paper proposes an exposure correction method called luminance component analysis (LCA). LCA applies the orthogonal constraint to a U-Net structure to decouple luminance-related and luminance-unrelated features. With decoupled luminance-related features, LCA adjusts only the luminance-related components while keeping the luminance-unrelated components unchanged. To optimize the orthogonal constraint problem, LCA employs a geometric optimization algorithm, which converts the constrained problem in Euclidean space to an unconstrained problem in orthogonal Stiefel manifolds. Extensive experiments show that LCA can decouple the luminance feature from the RGB color space. Moreover, LCA achieves the best PSNR (21.33) and SSIM (0.88) in the exposure correction dataset with 28.72 FPS.
CapHDR2IR: Caption-Driven Transfer from Visible Light to Infrared Domain
Venue: arXiv cs.CV 2024
Year: 2024

Infrared (IR) imaging offers advantages in several fields due to its unique ability of capturing content in extreme light conditions. However, the demanding hardware requirements of high-resolution IR sensors limit its widespread application. As an alternative, visible light can be used to synthesize IR images but this causes a loss of fidelity in image details and introduces inconsistencies due to lack of contextual awareness of the scene. This stems from a combination of using visible light with a standard dynamic range, especially under extreme lighting, and a lack of contextual awareness can result in pseudo-thermal-crossover artifacts. This occurs when multiple objects with similar temperatures appear indistinguishable in the training data, further exacerbating the loss of fidelity. To solve this challenge, this paper proposes CapHDR2IR, a novel framework incorporating vision-language models using high dynamic range (HDR) images as inputs to generate IR images. HDR images capture a wider range of luminance variations, ensuring reliable IR image generation in different light conditions. Additionally, a dense caption branch integrates semantic understanding, resulting in more meaningful and discernible IR outputs. Extensive experiments on the HDRT dataset show that the proposed CapHDR2IR achieves state-of-the-art performance compared with existing general domain transfer methods and those tailored for visible-to-infrared image translation.
FineWeb-zhtw: Scalable Curation of Traditional Chinese Text Data from the Web
Venue: arXiv cs.CL 2024
Year: 2024

The quality and size of a pretraining dataset significantly influence the performance of large language models (LLMs). While there have been numerous efforts in the curation of such a dataset for English users, there is a relative lack of similar initiatives for Traditional Chinese. Building upon this foundation of FineWeb, we introduce FineWeb-zhtw, a dataset tailored specifically for Traditional Chinese users. We came up with multiple stages of meticulously designed filters to cater to the linguistic difference between English and Traditional Chinese, to ensure comprehensiveness and quality. We determined effectiveness from querying dataset samples with three main objectives. Our code and datasets are publicly available.
A Study on Unsupervised Domain Adaptation for Semantic Segmentation in the Era of Vision-Language Models
Venue: arXiv cs.CV 2024
Year: 2024

Despite the recent progress in deep learning based computer vision, domain shifts are still one of the major challenges. Semantic segmentation for autonomous driving faces a wide range of domain shifts, e.g. caused by changing weather conditions, new geolocations and the frequent use of synthetic data in model training. Unsupervised domain adaptation (UDA) methods have emerged which adapt a model to a new target domain by only using unlabeled data of that domain. The variety of UDA methods is large but all of them use ImageNet pre-trained models. Recently, vision-language models have demonstrated strong generalization capabilities which may facilitate domain adaptation. We show that simply replacing the encoder of existing UDA methods like DACS by a vision-language pre-trained encoder can result in significant performance improvements of up to 10.0% mIoU on the GTA5-to-Cityscapes domain shift. For the generalization performance to unseen domains, the newly employed vision-language pre-trained encoder provides a gain of up to 13.7% mIoU across three unseen datasets. However, we find that not all UDA methods can be easily paired with the new encoder and that the UDA performance does not always likewise transfer into generalization performance. Finally, we perform our experiments on an adverse weather condition domain shift to further verify our findings on a pure real-to-real domain shift.
Machine Learning for the Digital Typhoon Dataset: Extensions to Multiple Basins and New Developments in Representations and Tasks
Venue: arXiv cs.CV 2024
Year: 2024

This paper presents the Digital Typhoon Dataset V2, a new version of the longest typhoon satellite image dataset for 40+ years aimed at benchmarking machine learning models for long-term spatio-temporal data. The new addition in Dataset V2 is tropical cyclone data from the southern hemisphere, in addition to the northern hemisphere data in Dataset V1. Having data from two hemispheres allows us to ask new research questions about regional differences across basins and hemispheres. We also discuss new developments in representations and tasks of the dataset. We first introduce a self-supervised learning framework for representation learning. Combined with the LSTM model, we discuss performance on intensity forecasting and extra-tropical transition forecasting tasks. We then propose new tasks, such as the typhoon center estimation task. We show that an object detection-based model performs better for stronger typhoons. Finally, we study how machine learning models can generalize across basins and hemispheres, by training the model on the northern hemisphere data and testing it on the southern hemisphere data. The dataset is publicly available at \url{http://agora.ex.nii.ac.jp/digital-typhoon/dataset/} and \url{https://github.com/kitamoto-lab/digital-typhoon/}.
Unsupervised Event Outlier Detection in Continuous Time
Venue: arXiv cs.LG 2024
Year: 2024

Event sequence data record the occurrences of events in continuous time. Event sequence forecasting based on temporal point processes (TPPs) has been extensively studied, but outlier or anomaly detection, especially without any supervision from humans, is still underexplored. In this work, we develop, to the best our knowledge, the first unsupervised outlier detection approach to detecting abnormal events. Our novel unsupervised outlier detection framework is based on ideas from generative adversarial networks (GANs) and reinforcement learning (RL). We train a 'generator' that corrects outliers in the data with a 'discriminator' that learns to discriminate the corrected data from the real data, which may contain outliers. A key insight is that if the generator made a mistake in the correction, it would generate anomalies that are different from the anomalies in the real data, so it serves as data augmentation for the discriminator learning. Different from typical GAN-based outlier detection approaches, our method employs the generator to detect outliers in an online manner. The experimental results show that our method can detect event outliers more accurately than the state-of-the-art approaches.
Privacy Protection in Personalized Diffusion Models via Targeted Cross-Attention Adversarial Attack
Venue: arXiv cs.CV 2024
Year: 2024

The growing demand for customized visual content has led to the rise of personalized text-to-image (T2I) diffusion models. Despite their remarkable potential, they pose significant privacy risk when misused for malicious purposes. In this paper, we propose a novel and efficient adversarial attack method, Concept Protection by Selective Attention Manipulation (CoPSAM) which targets only the cross-attention layers of a T2I diffusion model. For this purpose, we carefully construct an imperceptible noise to be added to clean samples to get their adversarial counterparts. This is obtained during the fine-tuning process by maximizing the discrepancy between the corresponding cross-attention maps of the user-specific token and the class-specific token, respectively. Experimental validation on a subset of CelebA-HQ face images dataset demonstrates that our approach outperforms existing methods. Besides this, our method presents two important advantages derived from the qualitative evaluation: (i) we obtain better protection results for lower noise levels than our competitors; and (ii) we protect the content from unauthorized use thereby protecting the individual's identity from potential misuse.
TIFeD: a Tiny Integer-based Federated learning algorithm with Direct feedback alignment
Venue: arXiv cs.LG 2024
Year: 2024

Training machine and deep learning models directly on extremely resource-constrained devices is the next challenge in the field of tiny machine learning. The related literature in this field is very limited, since most of the solutions focus only on on-device inference or model adaptation through online learning, leaving the training to be carried out on external Cloud services. An interesting technological perspective is to exploit Federated Learning (FL), which allows multiple devices to collaboratively train a shared model in a distributed way. However, the main drawback of state-of-the-art FL algorithms is that they are not suitable for running on tiny devices. For the first time in the literature, in this paper we introduce TIFeD, a Tiny Integer-based Federated learning algorithm with Direct Feedback Alignment (DFA) entirely implemented by using an integer-only arithmetic and being specifically designed to operate on devices with limited resources in terms of memory, computation and energy. Besides the traditional full-network operating modality, in which each device of the FL setting trains the entire neural network on its own local data, we propose an innovative single-layer TIFeD implementation, which enables each device to train only a portion of the neural network model and opens the door to a new way of distributing the learning procedure across multiple devices. The experimental results show the feasibility and effectiveness of the proposed solution. The proposed TIFeD algorithm, with its full-network and single-layer implementations, is made available to the scientific community as a public repository.
Learning by Analogy: Enhancing Few-Shot Prompting for Math Word Problem Solving with Computational Graph-Based Retrieval
Venue: arXiv cs.CL 2024
Year: 2024

Large language models (LLMs) are known to struggle with complicated reasoning tasks such as math word problems (MWPs). In this paper, we present how analogy from similarly structured questions can improve LLMs' problem-solving capabilities for MWPs. Specifically, we rely on the retrieval of problems with similar computational graphs to the given question to serve as exemplars in the prompt, providing the correct reasoning path for the generation model to refer to. Empirical results across six math word problem datasets demonstrate the effectiveness of our proposed method, which achieves a significant improvement of up to 6.7 percent on average in absolute value, compared to baseline methods. These results highlight our method's potential in addressing the reasoning challenges in current LLMs.
Characterized Diffusion Networks for Enhanced Autonomous Driving Trajectory Prediction
Venue: arXiv cs.RO 2024
Year: 2024

In this paper, we present a novel trajectory prediction model for autonomous driving, combining a Characterized Diffusion Module and a Spatial-Temporal Interaction Network to address the challenges posed by dynamic and heterogeneous traffic environments. Our model enhances the accuracy and reliability of trajectory predictions by incorporating uncertainty estimation and complex agent interactions. Through extensive experimentation on public datasets such as NGSIM, HighD, and MoCAD, our model significantly outperforms existing state-of-the-art methods. We demonstrate its ability to capture the underlying spatial-temporal dynamics of traffic scenarios and improve prediction precision, especially in complex environments. The proposed model showcases strong potential for application in real-world autonomous driving systems.
Distributed Online Optimization with Stochastic Agent Availability
Venue: arXiv cs.LG 2024
Year: 2024

Motivated by practical federated learning settings where clients may not be always available, we investigate a variant of distributed online optimization where agents are active with a known probability $p$ at each time step, and communication between neighboring agents can only take place if they are both active. We introduce a distributed variant of the FTRL algorithm and analyze its network regret, defined through the average of the instantaneous regret of the active agents. Our analysis shows that, for any connected communication graph $G$ over $N$ agents, the expected network regret of our FTRL variant after $T$ steps is at most of order $(κ/p^2)\min\big\{\sqrt{N},N^{1/4}/\sqrt{p}\big\}\sqrt{T}$, where $κ$ is the condition number of the Laplacian of $G$. We then show that similar regret bounds also hold with high probability. Moreover, we show that our notion of regret (average-case over the agents) is essentially equivalent to the standard notion of regret (worst-case over agents), implying that our bounds are not significantly improvable when $p=1$. Our theoretical results are supported by experiments on synthetic datasets.
O1 Replication Journey -- Part 2: Surpassing O1-preview through Simple Distillation, Big Progress or Bitter Lesson?
Venue: arXiv cs.CL 2024
Year: 2024

This paper presents a critical examination of current approaches to replicating OpenAI's O1 model capabilities, with particular focus on the widespread but often undisclosed use of knowledge distillation techniques. While our previous work explored the fundamental technical path to O1 replication, this study reveals how simple distillation from O1's API, combined with supervised fine-tuning, can achieve superior performance on complex mathematical reasoning tasks. Through extensive experiments, we show that a base model fine-tuned on simply tens of thousands of samples O1-distilled long-thought chains outperforms O1-preview on the American Invitational Mathematics Examination (AIME) with minimal technical complexity. Moreover, our investigation extends beyond mathematical reasoning to explore the generalization capabilities of O1-distilled models across diverse tasks: hallucination, safety and open-domain QA. Notably, despite training only on mathematical problem-solving data, our models demonstrated strong generalization to open-ended QA tasks and became significantly less susceptible to sycophancy after fine-tuning. We deliberately make this finding public to promote transparency in AI research and to challenge the current trend of obscured technical claims in the field. Our work includes: (1) A detailed technical exposition of the distillation process and its effectiveness, (2) A comprehensive benchmark framework for evaluating and categorizing O1 replication attempts based on their technical transparency and reproducibility, (3) A critical discussion of the limitations and potential risks of over-relying on distillation approaches, our analysis culminates in a crucial bitter lesson: while the pursuit of more capable AI systems is important, the development of researchers grounded in first-principles thinking is paramount.
Multi-Resolution Generative Modeling of Human Motion from Limited Data
Venue: arXiv cs.CV 2024
Year: 2024

We present a generative model that learns to synthesize human motion from limited training sequences. Our framework provides conditional generation and blending across multiple temporal resolutions. The model adeptly captures human motion patterns by integrating skeletal convolution layers and a multi-scale architecture. Our model contains a set of generative and adversarial networks, along with embedding modules, each tailored for generating motions at specific frame rates while exerting control over their content and details. Notably, our approach also extends to the synthesis of co-speech gestures, demonstrating its ability to generate synchronized gestures from speech inputs, even with limited paired data. Through direct synthesis of SMPL pose parameters, our approach avoids test-time adjustments to fit human body meshes. Experimental results showcase our model's ability to achieve extensive coverage of training examples, while generating diverse motions, as indicated by local and global diversity metrics.
Use-Inspired Mobile Robot to Improve Safety of Building Retrofit Workforce in Constrained Spaces
Venue: arXiv cs.RO 2024
Year: 2024

The inspection of confined critical infrastructure such as attics or crawlspaces is challenging for human operators due to insufficient task space, limited visibility, and the presence of hazardous materials. This paper introduces a prototype of PARIS (Precision Application Robot for Inaccessible Spaces): a use-inspired teleoperated mobile robot manipulator system that was conceived, developed, and tested for and selected as a Phase I winner of the U.S. Department of Energy's E-ROBOT Prize. To improve the thermal efficiency of buildings, the PARIS platform supports: 1) teleoperated mapping and navigation, enabling the human operator to explore compact spaces; 2) inspection and sensing, facilitating the identification and localization of under-insulated areas; and 3) air-sealing targeted gaps and cracks through which thermal energy is lost. The resulting versatile platform can also be tailored for targeted application of treatments and remediation in constrained spaces.
EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code
Venue: arXiv cs.SE 2024
Year: 2024

Automated detection of software vulnerabilities is critical for enhancing security, yet existing methods often struggle with the complexity and diversity of modern codebases. In this paper, we introduce EnStack, a novel ensemble stacking framework that enhances vulnerability detection using natural language processing (NLP) techniques. Our approach synergizes multiple pre-trained large language models (LLMs) specialized in code understanding CodeBERT for semantic analysis, GraphCodeBERT for structural representation, and UniXcoder for cross-modal capabilities. By fine-tuning these models on the Draper VDISC dataset and integrating their outputs through meta-classifiers such as Logistic Regression, Support Vector Machines (SVM), Random Forest, and XGBoost, EnStack effectively captures intricate code patterns and vulnerabilities that individual models may overlook. The meta-classifiers consolidate the strengths of each LLM, resulting in a comprehensive model that excels in detecting subtle and complex vulnerabilities across diverse programming contexts. Experimental results demonstrate that EnStack significantly outperforms existing methods, achieving notable improvements in accuracy, precision, recall, and F1-score. This work highlights the potential of ensemble LLM approaches in code analysis tasks and offers valuable insights into applying NLP techniques for advancing automated vulnerability detection.
Forest Covers and Bounded Forest Covers
Venue: arXiv cs.DS 2024
Year: 2024

We study approximation algorithms for the forest cover and bounded forest cover problems. A probabilistic $2+ε$ approximation algorithm for the forest cover problem is given using the method of dual fitting. A deterministic algorithm with a 2-approximation ratio that rounds the optimal solution to a linear program is given next. The 2-approximation for the forest cover is then used to give a 6-approximation for the bounded forest cover problem. The use of the probabilistic method to develop the $2+ε$ approximation algorithm may be of independent interest.
Location-Based Service (LBS) Data Quality Metrics and Effects on Mobility Inference
Venue: arXiv cs.CE 2024
Year: 2024

Today, GPS-equipped mobile devices are ubiquitous, and they generate Location-Based Service (LBS) data, which has become a critical resource for understanding human mobility. However, inherent limitations in LBS datasets, primarily characterized by discontinuity and sparsity, may introduce significant biases in representing individual movement patterns. This study develops data quality metrics for LBS data, examines their disparities among different populations, and quantifies their effects on inferred individual movement, stays in particular, in the Boston Metropolitan Area. We find that data from higher-income, more educated, and predominantly white census block groups (CBGs) show higher sampling rates but paradoxically lower data quality. This contradiction may stem from greater privacy awareness in these communities. Additionally, we propose a new framework to resample LBS data and quantitatively evaluate the inferential biases associated with data of varying quality. This versatile framework can analyze the impacts originating from different data processing workflows with LBS data. Using linear regression models with clustered standard error, we assess the impact of data quality metrics on inferring the number of stay points. The results show that better data quality, characterized by the number of observations and temporal occupancy, can significantly reduce the bias when calculating the stay points of an individual. The introduction of additional data quality metrics into the regression model can further explain the bias. Overall, this study provides insights into how data quality can influence our understanding of human mobility patterns, highlighting the importance of carefully handling LBS data in research.
Graph Pooling by Local Cluster Selection
Venue: arXiv cs.LG 2024
Year: 2024

Graph pooling is a family of operations which take graphs as input and produce shrinked graphs as output. Modern graph pooling methods are trainable and, in general inserted in Graph Neural Networks (GNNs) architectures as graph shrinking operators along the (deep) processing pipeline. This work proposes a novel procedure for pooling graphs, along with a node-centred graph pooling operator.
A Clinical Trial Design Approach to Auditing Language Models in Healthcare Setting
Venue: arXiv cs.CY 2024
Year: 2024

We present an audit mechanism for language models, with a focus on models deployed in the healthcare setting. Our proposed mechanism takes inspiration from clinical trial design where we posit the language model audit as a single blind equivalence trial, with the comparison of interest being the subject matter experts. We show that using our proposed method, we can follow principled sample size and power calculations, leading to the requirement of sampling minimum number of records while maintaining the audit integrity and statistical soundness. Finally, we provide a real-world example of the audit used in a production environment in a large-scale public health network.